Long-Term Pavement Performance Program Research Articles

Comparative study of moisture and stress dependent unbound material modulus for flexile pavement response modeling

Moisture can significantly impact the resilient modulus of pavement base and subgrade. Although various resilient modulus models exist, their accuracy in predicting pavement responses have not been examined and verified. This study aims to compare the accuracy of pavement response modeling using different resilient modulus, considering moisture and stress dependent unbound material modulus. Comprehensive pavement information including pavement structure, viscoelastic property of asphalt concrete, unbound material properties, moisture gradient, water table, temperature gradient and FWD deflections were collected from Long-Term Pavement Performance (LTPP) program. Based on collected data, finite element models were developed to predict pavement deflections under FWD load and pavement responses under traffic loading, respectively. The results show that the moisture-suction-stress dependent (MSS) model has the best prediction accuracy as compared to field measurements, followed by the Witczak model, while the generalized constitutive model displayed the largest deviation. Under traffic loading, the critical pavement responses predicted from Witczak and generalized constitutive model can have a significant deviation from those predicted from MSS model. The generalized constitutive model was found to constantly overestimate the responses, and the deviation of Witczak model depends on other factors such as pavement structure. Using results from MSS as the baseline, these deviations were found to result in up to 50% errors in service life of selected pavements in this study. These findings highlight the importance of taking matric suction into account for accurate modeling of flexible pavement responses.

Performance prediction for network-level flexible pavements using an optimised ANN approach

ABSTRACT Considering that the road network shares traffic flow as a system, this study aimed to predict the network-level flexible pavement performance, using an optimised artificial neural network (ANN) approach to predict the pavement performance of each individual road, and the Ford-Fulkerson algorithm to determine the weight coefficients of roads. ANNs were developed to predict distress conditions, functional conditions and structural performance using the inputs of the pavement age and structural, traffic and climatic conditions. ANNs were trained with Long-Term Pavement Performance (LTPP) program data, and almost all the coefficient of determination values between ANN outputs and measured results are larger than 0.9. This ANN approach was optimised by establishing grey models to provide predictive short-term performance data for training ANNs, and employing Kalman Filter to modify the long-term performance prediction. Applications in typical LTPP sections validated the effectiveness of the optimisation method. This study used the reduction in road network capacity due to the assumed unavailability of a certain road to quantify its role (weight coefficient), which was calculated using the Ford-Fulkerson algorithm. This network-level performance prediction approach was applied in a 25-expressways network in Shanghai, which validated the good generalisation capabilities of ANNs and feasibility of the Ford-Fulkerson algorithm.

Preventive Maintenance Decision-Making Optimization Method for Airport Runway Composite Pavements

Long-term preventive maintenance planning using finite annual budgets is vital for maintaining the service performance of airport runway composite pavements. Using the pavement condition index (PCI) to quantify composite pavement performance, this study investigated the PCI deterioration tendencies of middle runways, terminal runways, and taxiways and developed prediction models related to structural thickness and air traffic. Performance jump (PJ) and deterioration rate reduction (DRR) were used to measure maintenance benefits. Based on 112 composite pavement sections in the Long-term Pavement Performance Program, this study analyzed the influences of five typical preventive maintenance technologies on PJ, DRR, and PCI deterioration rates. The logarithmic regression relationship between PJ and PCI was obtained. For sections treated with crack sealing and crack filling, the DRR was nearly 0. For sections treated with fog seal, thin HMA overlay, and hot-mix recycled AC, the DRR was 0.2, 0.7, and 0.8, respectively. To solve the multi-objective maintenance problem, this study proposed a decision-making optimization method based on dynamic programming, and the solution algorithm was optimized, which was applied in a five-year maintenance plan. Considering different PCI deterioration tendencies of airport regions, as well as PJ, DRR, and costs of maintenance technologies, the preventive maintenance decision-making optimization method meets performance and financial requirements sufficiently.

Machine Learning Modeling of Wheel and Non-Wheel Path Longitudinal Cracking

Roads degrade over time due to various factors such as traffic loads, environmental conditions, and the quality of materials used. Significant investments have been poured into road construction globally, necessitating regular evaluations and the implementation of maintenance and rehabilitation (M&R) strategies to keep the infrastructure performing at a satisfactory level. The development and refinement of performance prediction models are essential for forecasting the condition of pavements, especially to address longitudinal cracking distress, a major issue in thick asphalt pavements. This research leverages multiple machine learning methods to create models predicting non-wheel path (NWP) and wheel path (WP) longitudinal cracking using data from the Long-Term Pavement Performance (LTPP) program. This study highlights the marked differences in distress conditions between WP and NWP, underscoring the importance of precise models that cater to their unique features. Aging trends for both types of cracking were identified through correlation analysis, showing an increase in WP cracking with age and a higher initial International Roughness Index (IRI) linked to NWP cracking. Factors such as material characteristics, kinematic viscosity, pavement thickness, air voids, particle size distribution, temperature, KESAL, and asphalt properties were found to significantly influence both WP and NWP cracking. The Exponential Gaussian Process Regression (GPR) emerged as the best model for NWP cracking, showcasing exceptional accuracy with the lowest RMSE of 89.11, MSE of 7940.72, and an impressive R-Squared of 0.63. For WP cracking, the Squared Exponential GPR model was most effective, with the lowest RMSE of 12.00, MSE of 143.93, and a high R-Squared of 0.62. The GPR models, with specific kernels for each cracking type, proved their adaptability and efficiency in various pavement scenarios. A comparative analysis highlighted the superiority of our new machine learning model, which achieved an R2 of 0.767, outperforming previous empirical models, demonstrating the strength and precision of our machine learning approach in predicting longitudinal cracking.

Predicting Rutting Development of Pavement with Flexible Overlay Using Artificial Neural Network

Pavement maintenance and repair is a crucial part of pavement management systems. Accurate and reliable pavement performance prediction is the prerequisite for making reasonable maintenance decisions and selecting suitable repair schemes. Rutting deformation, as one of the most common forms of asphalt pavement failures, is a key index for evaluating the pavement performance. To ensure the accuracy of the commonly used prediction models, the input parameters of the models need to be understood, and the coefficients of the models should be locally calibrated. This paper investigates the prediction of the rutting development of pavements with flexible overlays based on the data of the Canadian Long-Term Pavement Performance (C-LTPP) program. Pavement performance data that may be related to rutting were extracted from the survey of Dipstick for data analysis. Then, an artificial neural network (ANN) was adopted to analyze the factors affecting the rut depth, and to establish a model for the rutting development of pavements with flexible overlays. The results of the sensitivity analysis indicate that rutting is not only affected by traffic and climatic conditions, but it is also greatly affected by the thickness of the surface layer and voids in the mixture. Finally, a rutting evaluation index was provided to describe the rutting severity, and the threshold of the pavement maintenance time was proposed based on the prediction results. These results provide a basis for predicting rut development and pavement maintenance.

LTPP data-based investigation on asphalt pavement performance using geospatial hot spot analysis and decision tree models

Environmental factors and truck traffic loads have significant impacts on asphalt pavement performance. This study implements geospatial hot spot, correlation, and decision tree analyses to investigate the impacts of environmental factors and truck traffic loads on asphalt pavement performance. A pavement database with 1725 asphalt pavement sections from the Long-Term Pavement Performance (LTPP) program was built and analyzed using geospatial hot spot analysis to characterize the spatial patterns of environmental factors, truck traffic loads, and asphalt pavement distresses in different climatic regions across the United States and Canada. The statistical correlation analysis was conducted to identify significant correlations among hot spots of environmental factors, truck traffic loads, and asphalt pavement distresses. The decision tree model, which is a supervised machine learning method, was used to assess pavement performance in an area that is associated with higher risks of distress based on contributing environmental and traffic conditions. The hot spot analysis showed that the pavement sections located in the dry no-freeze region had higher percentages of hot spots of truck traffic loads and associated load-induced distresses, such as fatigue cracking, longitudinal wheel path cracking, and rutting. In the dry no-freeze region, higher percentages of pavement sections were also classified as hot spots of transverse cracking. The pavement sections in the wet freeze region are more likely to experience longitudinal non-wheel path cracking and surface roughness. The decision tree models were built to identify the likeliness of hot spots of asphalt pavement distresses using environmental factors and truck traffic loads. These decision tree models provide enhanced decision-making information in pavement design and maintenance.

Incorporation of Pavement Dynamic Loading in Mechanistic–Empirical Design Frameworks

A framework for incorporating dynamic loading in pavement design is introduced. The framework is composed of five main steps. Firstly, input parameters, such as traffic characteristics, material properties, and the pavement configuration, are determined. Both primary and secondary data were obtained from the literature in this step. Secondly, a database of critical pavement responses, used by AASHTOWare software’s transfer functions to calculate pavement damage, was built using advanced pavement finite element models after the loading input was defined. The database includes various pavement configurations, axle configurations (single and tandem), pavement material properties, temperatures, loading conditions, and tire types (single steering, dual, and wide-base). AASHTOWare’s transfer function predicts pavement damage from pavement responses and is used to calculate the international roughness index (IRI) in the third and fourth steps, respectively. Finally, the load spectrum is adjusted to incorporate roughness-induced dynamic loading once the IRI exceeds 95 in./mi. A simple analytical dynamic-loading model was developed, based on mechanistic truck–trailer results. The model is a function of the tire configuration, speed, and IRI. Multiple case studies were performed, considering various pavement configurations, material properties, average annual daily traffic values, and dynamic-loading percentiles. Results showed that dynamic loading had the most significant impact on fatigue life, followed by rutting potential. The AASHTOWare’s transfer functions were found to be insensitive to an increase in loading when considering thick-pavement structures. The outcome of this effort was validated using the measured and predicted IRI from the Specific Pavement Study sections, which are part of the Long-Term Pavement Performance program.

Understanding the effects of structural factors and traffic loading on flexible pavement performance

Pavement damage increases rapidly with vehicle loads. The deterioration in pavements over time is caused by various factors but traffic loading plays a significant role in the consumption of pavement life. heavy vehicle loads, in addition to the insufficient structural capacity of pavements, are a major contributor to poor road conditions. Alligator cracking, rutting, and roughness are among the various types of distresses that are performance measures of road conditions and are the primary focus of this study. Thus, in order to improve the understanding of pavement deterioration in terms of these distresses, this paper assesses the effect of different structural factors, traffic loading and environmental conditions on pavement performance. In this study, the base type, hot mix asphalt concrete (HMAC) layer thickness, base layer thickness, traffic loading, age, and environmental conditions (precipitation and temperature) are chosen as major contributing factors for the analysis of pavement performance. Generalized linear models (GLM), binary logistic regression (BLR), and random forest (RF) approaches are employed to evaluate the data from the Long-Term Pavement Performance Program (LTPP). Cross validation technique is used to compare the efficacy of regression models (GLM, BLR) with RF. Numerous performance indicators demonstrated that RF surpasses other models in terms of accuracy and identification of influencing variables. The result from the study shows that the important design parameters that have considerable effects on rutting, roughness, and alligator cracking are the base thickness and base type. Traffic loading, pavement age and environmental factors (precipitation and temperature) are critical factors and have a significant impact on pavement performance. This study brings a new idea of using statistical methods to analyze the effects of structural factors and loading on flexible pavements.

Nomogram for Predicting Asphalt Pavement Roughness After Preventive Maintenance Based on Long-Term Pavement Performance Data

Relative effectiveness has been used as the primary criterion when choosing pavement preventive maintenance (PM) treatments; however, the most effective treatment is not necessarily the most cost-effective treatment because of the higher cost. Cost-effectiveness should be determined by predictive models of PM-treated pavement performance, which has rarely been investigated. In this study, a predictive model for post-treatment pavement roughness—as defined by international roughness index (IRI)—was established utilizing the data from the Long-Term Pavement Performance Program (LTPP) Specific Pavement Study 3 (SPS-3). The generalized least squares (GLS) model was employed to improve the predictive performance by exploiting the characteristics of LTPP panel data. A nomogram was also provided to help highway agencies manually obtain the predicted post-treatment IRI values. Results show that post-treatment IRI was significantly higher in dry and non-freeze areas than in other climate areas. The effect of pavement structure on post-treatment IRI was time-dependent; it was insignificant at the beginning and gradually increased after 4 years. Although post-treatment IRI was affected by pavement structures and climate, the relative effectiveness of different PM treatments was only related to the pre-treatment IRI. Thin overlay significantly improved the pavement IRI, and when pre-treatment IRI was 2.0 m/km, the post-treatment IRI of the thin overlay would be reduced to 0.6 times that of the control. However, there was no significant difference in pavement IRI between different seal treatments.

Analysis on Effects of Joint Spacing on the Performance of Jointed Plain Concrete Pavements Based on Long-Term Pavement Performance Database.

With the day-night temperature and moisture levels changing every day, expansion and shrinkage of concrete slabs is always occurring; therefore, joints provide extra room for concrete slab deformation. The joint spacing in jointed plain concrete pavement (JPCP) is continuously affecting long-term pavement behaviors. In this study, data from the Long-Term Pavement Performance (LTPP) program were analyzed, and the behaviors of JPCP with different joint spacings were compared to discover the joint spacing effects. Since LTPP has an enormous database, three representative sections located in different states were selected for analysis, where the variable factors such as temperature, moisture, and average annual daily truck traffic (AADTT) were almost the same between the three sections. Three different joint spacings, including 15 ft (4.5 m), 20 ft (6 m), and 25 ft (7.5 m), were compared based on the collected LTPP data. The involved long-term pavement performances, such as average transverse cracking (count), average JPCP faulting, international roughness index (IRI), and falling weight deflectometer (FWD) deflections were compared between JPCP with different joint spacings. Based on the comparative analysis, the JPCP constructed with a 15 ft joint spacing demonstrated the best long-term performance. It showed no transverse cracking, the lowest average JPCP faulting, the best IRI value, and the smallest FWD deflection during the entire in-service period. With proper joint spacing, the cost of road maintenance throughout the life cycle could be significantly reduced due to there being less distress. Therefore, it is recommended to optimize the joint spacing to about 15 ft in JPCP in future applications.

Improving asphalt mix design considering international roughness index of asphalt pavement predicted using autoencoders and machine learning

Scientific asphalt mix design can improve the engineering properties of asphalt mixture, thus further slowing the growth of international roughness index (IRI). To avoid the occurrence of high IRI values in pavement design life, this study improved the current asphalt mix design by introducing a machine learning (ML) model to predict pavement IRI. The data relating to the IRI of flexible pavements were collected from the long-term pavement performance (LTPP) program database. A total of 37 input features about climate condition, traffic, pavement structure, and pavement materials properties were selected based on backward elimination and recursive feature elimination methods. The effect of five different dimensionality reduction techniques on the performance of ML models was analyzed. Six different ML algorithms including support vector regression (SVR), random forest, artificial neural networks (ANN), Gaussian process regression (GPR), Extra-trees, and gradient boosting (GB) were trained to predict IRI, and the performance metrics (R2, RMSE, MAE, MAPE, and SI) of these ML models were computed and compared. The IRI criterion and the best IRI prediction model were then added to the asphalt mix design procedure. The results showed that compared to other dimensionality reduction methods, the combination of autoencoders makes ANN and GB perform better. The AE-GPR was the best approach to predicting IRI (R2 = 0.94). The proposed mix design procedure was operated to design the asphalt concrete for the surface layer of a construction project. The predicted IRIs of the initial trial blends (including the traditionally designed mix) do not exceed the range of the IRI criterion in the procedure, and the recommended mixture remains the same as the traditionally designed one.

Improving asphalt mix design by predicting alligator cracking and longitudinal cracking based on machine learning and dimensionality reduction techniques

• Reasonable asphalt mixture design can mitigate the cracking problems. • Alligator cracking (AC) and longitudinal (LC) cracking are predicted from asphalt mixture properties. • The effect of three different dimension reduction methods on the performance of ML models is analyzed. • ML algorithms includes Support vector regression, Artificial Neural Networks, Kernel ridge regression, Gradient boosting, Extra-trees, eXtreme Gradient Boosting. • The paper improves the current asphalt mix design by adding the criteria of AC and LC and ML models. The asphalt mix design based on traditional laboratory fatigue cracking tests of asphalt mixture is not reasonable due to the difficulty of simulating the circumstance where asphalt mixture experiences in the pavement structure and under realistic climate and traffic conditions. The study aims to mitigate the cracking problems during the pavement design life, by improving the asphalt mix design process by introducing machine learning (ML) models, which are used to predict alligator cracking (AC) and longitudinal cracking (LC), and their criteria (required ranges). The data containing AC and LC were extracted from the NCHRP 1-37A report and the long-term pavement performance (LTPP) program. A total of 33 input features about climate condition, traffic, pavement structure, and materials properties of each pavement layer were selected. Support Vector Regression (SVR), Artificial Neural Networks (ANN), Kernel ridge regression (KRR), Gradient boosting (GB), Extra-trees, and eXtreme Gradient Boosting (XGBoost) were used. Meanwhile, three dimensionality reduction approaches, including Auto-Encoder (AE), Principal Component Analysis (PCA), and Recursive feature elimination with Random Forest, were combined with the six ML algorithms (18 hybrid models produced) in order to decrease the computation complexity and search for the optimum model. The 24 models (6 basic ML models plus 18 hybrid models) were trained, and their hyperparameters were tuned using Bayesian optimization. Different evaluation criteria R 2 , RMSE, MAE, SMAPE, and SI are calculated to evaluate the performance of these models. The results of the study showed that except for ANN and SVR, the basic ML models can get better for predicting AC and LC when they are combined with PCA or AE. Based on the performance indices, the optimum among these developed models proved to be PCA-ANN for predicting AC (R 2 = 0.84) and LC (R 2 = 0.83). Compared to other pavement layers, the asphalt surface course is the highest contributor to AC and LC. The improved mix design process was implemented to determine the mix proportion for the asphalt surface course of a construction project and Mix-2 with 5.1 % asphalt content was recommended.

Predicting Pavement Condition Index Using Machine Learning Algorithms and Conventional Techniques

Government agencies and transportation engineers use pavement management systems (PMS) to evaluate pavement performance and keep pavement above the minimum acceptable performance standards. The Pavement Condition Index (PCI) and the international roughness index (IRI) are among the most commonly used indices to evaluate pavement conditions. Due to IRI data collection being more accessible and less expensive than collecting pavement distress data, this study aims to develop PCI models that can successfully estimate the PCI values based on IRI for flexible pavement using two Machine Learning techniques (ML), namely: Random Forest (RF), and Support Vector Machine (SVM), and three conventional techniques, namely: linear, quadratic, and cubic regression. The study was carried out with the database collected from the Long-Term Pavement Performance (LTPP) program. The results of the dataset reveal that both ML models (RF and SVM) have strong prediction ability with high values of coefficient of determination (R^2 = 99.7 and 96.8) %, and low values of Root Mean Squared Error (RMSE = 1.095 and 3.569) % and Mean Absolute Error (MAE = 0.474 and 2.244). In conclusion, the goodness of fit of the proposed ML models was compared with conventional techniques models previously developed. The results showed that the ML models yielded higher prediction accuracy than conventional techniques.

Survival Analysis for Asphalt Pavement Performance and Assessment of Various Factors Affecting Fatigue Cracking Based on LTPP Data

Pavement performance is the ability of pavement to remain in an acceptable condition to serve the intended users over a period of time. There are several principal, combined factors that affect flexible pavement performance such as environmental conditions, pavement materials, and traffic loads. Vehicle overloading is considered one of the most significant causes of accelerating flexible pavement deterioration, reducing the pavement’s design life, and affecting the overall sustainability of the pavement system. Therefore, researchers are continuously examining pavement systems with a view to finding the most suitable solutions for sustainable development in road construction systems in order to reduce both costs and pollution. In this study, we present a framework to conduct nonparametric and parametric survival analysis for asphalt pavement test sections, to assess the influence of using reclaimed asphalt pavement (RAP) on fatigue service life, to indicate the most significant subset of risk factors (covariates), and to study the effect of overweight axles on flexible pavement performance. All the data concerned were extracted from the long-term pavement performance (LTPP) program. The Kaplan–Meier (KM) survival probability curves of multiple pavement distresses were developed to compare the failure probability for various distresses and to determine the median survival time for each distress. The fatigue survival curves for the test sections using RAP and virgin materials were developed separately and the equality of the two survival curves was tested and affirmed. Several parametric survival analyses were conducted to select the most significant subset of covariates. For fatigue cracking and, after dropping the insignificant predictors, a model was developed to show the quantitative relationship between fatigue failure time and potentially influential factors. The analysis indicated that the increase in the percentage of overloaded axles from 0% to 20% can reduce the fatigue survival life of flexible pavement by up to 55%. In the absence of overweight axles, a one-inch increase in asphalt layer thickness can extend the fatigue service life by about half a year. However, in the presence of 20% of overweight axles, a one-inch increase in thickness can extend the fatigue service life by only 0.22 years. Therefore, additional virgin materials and resources are needed to maintain traffic conditions in the road network and to compensate for the reduction in fatigue service life. Moreover, the effect of the increase in overweight axles from 0% to 15% on reducing the fatigue survival life is found to be similar to the effect of increasing the AADTT tenfold. Therefore, the sustainability of pavement is directly affected by the fatigue survival life.

Superpave performance graded asphalt binder selection for asphalt mixture design in Ghana

The Superior Performing Asphalt Pavements (Superpave) system – comprising asphalt binder grading, mixture design and mixture performance evaluation – has been in existence for nearly 30 years and has been implemented or evaluated for possible adoption in several countries to help address fatigue cracking, rutting and thermal cracking. These asphalt pavement distresses minimize pavement integrity to cause significant increase in maintenance cost, safety risks and roughness. Premature fatigue cracking and rutting failures are common in Ghana. Currently, only the mixture design component of the Superpave system is an option in the country's standard construction specification; asphalt binder selection is based on viscosity grading. Recently, very few projects have utilized Superpave-graded binders, and some of the binder grade selections were problematic. The use of Superpave-graded binders on high-volume roads is anticipated to become a common practice in the foreseeable future. However, there is no road agency guidance document on Superpave binder grade selection in Ghana currently, and this study sought to fill the gap. Such a guidance document will facilitate binder grade selection and streamline binder grade selection practices. This study utilized 42 years (1979–2020) of air temperature data collected from 24 weather stations across the country and measured asphalt pavement temperature to evaluate four existing Superpave binder grade prediction models. The model derived from the Long-Term Pavement Performance program better represented Ghana's climatic conditions and suggested the country may be partitioned into two Superpave binder grade selection zones: the Northern Savannah zone and the Transition/Forest/Coastal zone. At 98% reliability level, a binder grade of PG 70–10 was determined for the northern sector and PG 64–10 for the rest of the country. These are base binder grades (based on weather only) which must be adjusted for traffic conditions at the project site. Research should focus on local model formulation for refined Superpave binder grade selection and, during construction, selected binder grades must be verified through laboratory testing. Proper binder grade selection must be supplemented with effective pavement design, construction, axle load control and maintenance for good pavement performance and longevity.

A Case-Based Reasoning and Random Forest Framework for Selecting Preventive Maintenance of Flexible Pavement Sections

Pavement maintenance decision-making is receiving significant attention in recent research, since pavement infrastructure is aging and deteriorating. The decision-making process is mainly related to selecting the most appropriate maintenance intervention for pavement sections to ensure performance and enhance safety. Several preventive maintenance methods have been proposed in the previous studies, yet the potential of implementing Case-Based Reasoning (CBR) in pavement maintenance decision-making has been investigated rarely. The CBR is an artificial intelligence technique, it is knowledge-based on several known cases, which are used to adapt a solution for a new case through retrieving similar cases. This research introduces the CBR to the area of pavement management to select the most appropriate preventive maintenance strategy for flexible pavement sections. The needed database was extracted from maintenance cases at Long-Term Pavement Performance Program. The criteria used to characterize condition of each section were identified based on the common practices in pavement maintenance published in the literature and implemented in the field. To assign weights to the selected criteria, different machine learning techniques were tested, and subsequently, Random Forest (RF) algorithm was selected to be integrated with the proposed CBR method producing the CBR-RF framework. A case study was analyzed to validate the proposed framework and a sensitivity analysis was conducted to assess the influence of each criterion on case retrieval accuracy and overall framework performance. Results indicated that the CBR-RF approach could assist effectively in the preventive maintenance decision-making with regard to new cases by learning from the previous similar cases. Accordingly, several agencies can depend on the proposed framework, while facing similar decision-making problems. Future research can compare the CBR-RF framework with other machine learning algorithms using the same dataset included in this research.

Traffic Data and Summary Statistics Available through the Long-Term Pavement Performance Program for Pavement Modeling and Beyond

This paper synthesizes traffic data and statistical parameters available through the Long-Term Pavement Performance Program (LTPP), provides recommendations to pavement engineers and researchers about the traffic parameters most suitable for different pavement analyses, and identifies LTPP database tables containing such parameters. The paper also contains information about LTPP traffic data collection methods and recommendations for assessing the rationality of truck weight data collected by weigh-in-motion (WIM) equipment. In summary, this paper was developed to highlight the wealth of truck traffic data available through LTPP and to serve as a resource document for pavement researchers, designers, and transportation planning professionals interested in LTPP truck traffic data and summary parameters characterizing truck traffic.

Relationship between backcalculated dynamic modulus, estimated dynamic modulus, and fatigue damage in asphalt concrete

ABSTRACT There are several methods for determining the stiffness of asphalt concrete in an existing pavement. The three primary methods are dynamic modulus testing in the laboratory, predictive equations, and falling weight deflectometer (FWD) testing. The Pavement ME asphalt over asphalt (AC/AC) overlay design procedure uses multiple methods to characterise fatigue damage in the existing asphalt concrete. This paper examines the relationship between backcalculated dynamic modulus, estimated dynamic modulus, and fatigue damage in in-situ asphalt concrete. FWD load magnitude and asphalt temperature, both of which have been shown to affect the relationship between backcalculated and estimated dynamic modulus, are also considered. A statistical analysis is performed using data from the Long-Term Pavement Performance Programme (LTPP). Analyses are performed within individual LTPP sections and using pooled data from multiple pavement sections. It is determined that the relationship between backcalculated dynamic modulus and estimated dynamic modulus is significantly related to the amount of fatigue damage in the in-situ asphalt concrete. However, there is also a threshold of fatigue damage above which the relationship between backcalculated modulus, estimated modulus, and fatigue damage is no longer significant.

Modeling the Effects of Asphalt Overlay Design on Pavement Distress Initiation with Random Parameters Hazard-Based Duration Models

Pavement deterioration model is a key input to a pavement management system that optimizes pavement maintenance and rehabilitation (M&R) planning. As a typical pavement M&R technique, asphalt overlay is commonly implemented to restore pavement performance. However, the impact of its design factors on pavement deterioration is not well documented in existing publications. This study aimed to investigate the effects of asphalt overlay design on the initiation of various types of cracking and severe rutting with consideration of endogenous bias and unobserved heterogeneity. A series of random parameters Weibull duration models were developed based on 317 asphalt overlay projects, which were collected from the Long-Term Pavement Performance program. Endogenous overlay design, overlay thickness, milling indicator, and overlaid pavement deflection were identified to represent their heterogeneity caused by unobserved factors over highway sections. Based on analysis results, the endogenous overlay design was shown to underestimate the service life of overlaid pavement. The occurrence probability of overlaid pavement distress would significantly decrease with an increase of asphalt overlay thickness. The application of 30% reclaimed asphalt pavement materials would retard the occurrence of severe rutting, but accelerate the occurrence of all types of cracking. Relative to asphalt overlay, milling and overlay can significantly reduce the hazard rate of severe rutting and all types of cracking. In addition, existing pavement condition, overlaid pavement deflection, traffic volume, and climate factors were identified to have significant effects on the distress initiation in overlaid pavements.

Effects of maintenance, traffic and climate condition on International Roughness Index of flexible pavement

ABSTRACT This paper focuses on developing the predictive models for International Roughness Index (IRI) of flexible pavement using several soft computing systems. About 564 sections with 4229 observations of the flexible pavement from the database of the long-term pavement performance (LTPP) program were used for developing and validating the prediction models. Equivalent single axle load (ESAL), structural number (SN), thickness, resilient modulus (MR) of the base layer, age, annual truck volume trend, total annual precipitation, freezing index, and maintenance treatments were the predictive attributes used in models. Various statistical criteria were used for evaluating the performance of the developed models, comprehensively. The results showed that the models developed in the current study are fairly promising tools for the prediction of IRI and capable of representing the complex relationship between the IRI and the predictive attributes selected. The models created through the Gradient Boosting Method (GBM) and Random Forest (RF), which were markedly outperformed the results of other methods, produced a correlation coefficient (R) greater than 0.90 and mean absolute error lower than 0.2. Furthermore, the SN was found to be the most important parameter for the IRI according to the variable importance score of the best performing prediction model.